USP19 and RPL23 as candidate prognostic markers in advanced-stage high-grade serous ovarian carcinoma
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ABSTRACT:
PROVIDER: PRJNA697947 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13579634_1.fastq.gz | Fastqsanger.gz | |||
| SRR13579634_2.fastq.gz | Fastqsanger.gz | |||
| SRR13579635_1.fastq.gz | Fastqsanger.gz | |||
| SRR13579635_2.fastq.gz | Fastqsanger.gz | |||
| SRR13579636_1.fastq.gz | Fastqsanger.gz |
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Project description:Ovarian cancer is one of the leading causes of death among patients with gynecological malignancies worldwide. To identify prognostic markers for ovarian cancer, we performed RNA-sequencing and analyzed the transcriptome data from 51 patients who received conventional therapies for high-grade serous ovarian carcinoma (HGSC). Patients with early-stage (I or II) HGSC exhibited higher immune gene expression than patients with advanced stage (III or IV) HGSC. To predict the prognosis of HGSC patients, we created machine learning-based models and identified RPL23 and USP19 as candidate prognostic markers. Specifically, patients with higher RPL23 mRNA levels had a worse prognosis and patients with higher USP19 mRNA levels had a better prognosis. This model was then used to analyze HGSC patient data hosted on The Cancer Genome Atlas; this exercise validated the prognostic abilities of these two genes with respect to patient survival. Taken together, the transcriptome profiles of RPL23 and USP19 determined using a machine-learning model could serve as prognostic markers in HGSC patients receiving conventional therapy.
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Project description:Low-grade ovarian serous carcinomas are believed to arise via an adenoma-serous borderline tumor-serous carcinoma sequence. In this study, we found that advanced-stage, low-grade ovarian serous carcinomas both with and without adjacent serous borderline tumor shared similar regions of loss of heterozygosity. We then analyzed 91 ovarian tumor samples for mutations in TP53, BRAF, and KRAS. TP53 mutations were not detected in any serous borderline tumors (n = 30) or low-grade serous carcinomas (n = 43) but were found in 73% of high-grade serous carcinomas (n = 18). BRAF (n = 9) or KRAS (n = 5) mutation was detected in 47% of serous borderline tumors, but among the low-grade serous carcinomas (39 stage III, 2 stage II, and 2 stage I), only one (2%) had a BRAF mutation and eight (19%) had a KRAS mutation. The low frequency of BRAF mutations in advanced-stage, low-grade serous carcinomas, which contrasts with previous findings, suggests that aggressive, low-grade serous carcinomas are more likely derived from serous borderline tumors without BRAF mutation. In addition, advanced-stage, low-grade carcinoma patients with BRAF or KRAS mutation have a better apparent clinical outcome. However, further investigation is needed. Low-grade ovarian serous carcinomas are believed to arise via an adenoma-serous borderline tumor-serous carcinoma sequence. In this study, we found that advanced-stage, low-grade ovarian serous carcinomas both with and without adjacent serous borderline tumor shared similar regions of loss of heterozygosity. We then analyzed 91 ovarian tumor samples for mutations in TP53, BRAF, and KRAS. TP53 mutations were not detected in any serous borderline tumors (n = 30) or low-grade serous carcinomas (n = 43) but were found in 73% of high-grade serous carcinomas (n = 18). BRAF (n = 9) or KRAS (n = 5) mutation was detected in 47% of serous borderline tumors, but among the low-grade serous carcinomas (39 stage III, 2 stage II, and 2 stage I), only one (2%) had a BRAF mutation and eight (19%) had a KRAS mutation. The low frequency of BRAF mutations in advanced-stage, low-grade serous carcinomas, which contrasts with previous findings, suggests that aggressive, low-grade serous carcinomas are more likely derived from serous borderline tumors without BRAF mutation. In addition, advanced-stage, low-grade carcinoma patients with BRAF or KRAS mutation have a better apparent clinical outcome. However, further investigation is needed. Gene expression analysis was performed on five serous borderline tumors with BRAF mutation and five serous borderline tumors without BRAF mutation randomly. RNA was extracted from microdissected tumor cells. Expression profiling was carried out with Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays
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Project description:Ovarian cancer is one of the leading causes of deaths among patients with gynecological malignancies worldwide. In order to identify prognostic markers for ovarian cancer, we performed RNA-sequencing and analyzed the transcriptome data from 51 patients who received conventional therapies for high-grade serous ovarian carcinoma (HGSC). Patients with early-stage (I or II) HGSC exhibited higher immune gene expression than patients with advanced stage (III or IV) HGSC. In order to predict the prognosis of patients with HGSC, we created machine learning-based models and identified USP19 and RPL23 as candidate prognostic markers. Specifically, patients with lower USP19 mRNA levels and those with higher RPL23 mRNA levels had worse prognoses. This model was then used to analyze the data of patients with HGSC hosted on The Cancer Genome Atlas; this analysis validated the prognostic abilities of these two genes with respect to patient survival. Taken together, the transcriptome profiles of USP19 and RPL23 determined using a machine-learning model could serve as prognostic markers for patients with HGSC receiving conventional therapy.
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